Signals of interest in various data acquisition and processing devices are always affected by various interferences (noise) from natural and man-made sources. Be it a signal from a sensor, or a signal from a transmitter in a communication chain, the amount of noise affecting the signal needs to be reduced in order to improve the signal quality.
Since a signal of interest typically occupies a different and/or narrower frequency range than the noise, linear filters are applied to the incoming mixture of the signal and the noise in order to reduce the frequency range of the mixture to that of the signal. This reduces the power of the interference to a fraction of the total, limited to the frequency range of the signal.
However, the noise having the same frequency power spectrum can have various peakedness, and be impulsive or non-impulsive. For example, white shot noise is much more impulsive than white thermal noise, while both have identically flat power spectra. Linear filtering in the frequency domain does not discriminate between impulsive and non-impulsive noise contributions, and does not allow mitigation of the impulsive noise relative to the non-impulsive. In addition, reduction in the bandwidth of an initially impulsive noise by linear filtering makes the noise less impulsive, decreasing the ability to separate the signal from the noise based on their peakedness.
Effective suppression of impulsive interferences typically requires nonlinear means, for example, processing based on order statistics. These means can be employed either through digital signal processing, or in the analog signal chain. The nonlinear filters in the analog signal chain can range from simple slew rate limiting filters to more sophisticated analog rank filters described, for example, in U.S. Pat. Nos. 7,133,568 and 7,242,808, referenced as (Nikitin and Davidchack, 2006 and 2007), and U.S. Pat. Nos. 7,107,306, 7,418,469, and 7,617,270, referenced as (Nikitin, 2006, 2008, and 2009).
However, the practical use of nonlinear filters is limited as it typically results in complicated design considerations and in multiple detrimental effects on normal signal flow. These filters cause various nonlinear distortions and excessive attenuation of the signal, and their effect on the useful signal components is typically unpredictable and depends on the type and magnitude of the interfering signal.
A particular example of impulsive interference is the electromagnetic interference (EMI), also called radio frequency interference (RFI). It is a widely recognized cause of reception problems in communications and navigation devices.
EMI is a disturbance that affects an electrical circuit due to either conduction or radiation emitted from a source internal or external to the device. EMI may interrupt, obstruct, or otherwise degrade the effective performance of the device, and limit its link budget. The detrimental effects of EMI are broadly acknowledged in the industry and include: (i) reduced signal quality to the point of reception failure, (ii) increased bit errors which degrade the system resulting in lower data rates and decreased reach, and (iii) increased power output of the transmitter, which reduces the battery life and increases its interference with nearby receivers.
A major and rapidly growing source of EMI in communication and navigation receivers is other transmitters that are relatively close in frequency and/or distance to the receivers. Multiple transmitters and receivers are increasingly combined in single devices which produces mutual interference A typical example is a smartphone equipped with cellular, WiFi, Bluetooth, and GPS receivers. Other typical sources of strong EMI are on-board digital circuits, clocks, buses, and power supplies.
Most state-of-the-art analog mitigation methods of EMI focus on reducing the interference before it reaches the receiver, and none of these methods allows effective EMI filtering once it has entered the receiver chain. After the interference has entered the signal path, only computationally and silicon intensive nonlinear, non-real-time digital signal processing solutions are offered.
Other systems impeded by the impulsive noise and artifacts are various sensor systems, including all coherent imaging systems. A common example is various medical imaging systems such as ultrasonic, which are generally affected by multiplicative shot (or speckle) noise. Typically, various methods of reduction of the speckle noise involve non-real-time adaptive and non-adaptive speckle filtering of the acquired images, or multi-look processing.